AI Prediction of Device Components for Repair and Replacement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional device management approaches face challenges such as significant variation across repair sites and components, leading to lower repair yields, resource inefficiencies, and increased repeat return rates, particularly in repairing sophisticated devices like printed circuit boards (PCBs).
Innovation Solution
Utilizing artificial intelligence techniques to predict device components for repair and replacement by processing information related to device defects through clustering and deep learning-based classification, incorporating domain knowledge and historical data to identify the most probable components needing repair or replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional device management approaches are used for repairing device components, then repair processes can be performed, but significant variation across repair sites and components results in lower repair yields and increased repeat return rates
Solution Approach 1:
The system performs preliminary classification and prediction of device components requiring repair before the actual repair process. By using machine learning models to analyze defect information and predict affected components in advance, the system prepares repair plans beforehand, reducing variation and improving repair yields across different repair sites.
Solution Approach 2:
The system implements feedback mechanisms where repair outcomes and defect information are continuously collected and used to retrain and improve the machine learning models. This feedback loop enables the system to learn from past repairs and adapt to different device types and defect patterns, reducing variation across repair sites while maintaining high repair yields.
2Productivity
If conventional device management approaches are used, then repair processes can be performed, but resource-related inefficiencies occur due to manual identification and processing of defect information
Solution Approach 1:
The system replaces manual mechanical processes of defect analysis with automated machine learning-based classification and prediction systems. The ML models automatically process defect information, classify device components, and predict repair requirements, eliminating time-consuming manual analysis and significantly improving repair efficiency.
Solution Approach 2:
The system enables self-service automation where the machine learning models independently analyze defect information, identify affected components, and generate repair recommendations without human intervention. This automated self-service capability reduces both the time required for defect analysis and the labor resources needed for repair planning.
3Reliability
If conventional approaches are used for device component repair, then repairs can be performed, but increased repeat return rates occur which decrease available inventory and increase scrap rates
Solution Approach 1:
The system performs preliminary prediction of all device components that may require repair or replacement before the repair process begins. By using machine learning to forecast complete repair requirements in advance, the system ensures that all necessary components are prepared beforehand, preventing repeat returns caused by missing or incorrect parts, thereby maintaining inventory availability.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for predicting device components for repair and/or replacement using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining information pertaining to at least one device defect; defining multiple device component categories related to the device defect(s); determining one or more of the device component categories as associated with the device defect(s) by processing at least a first portion of the information using one or more artificial intelligence techniques; identifying one or more device components associated with at least a second portion of the information; predicting at least one of the identified device component(s), based on comparing the identified device component(s) and the one or more determined device component categories, as needing to be repaired and/or replaced in connection with at least a portion of the device defect(s); and performing one or more automated actions based on the predicting.